💻Installation
A step-by-step guide to install PyCaret in Python
Option 1: Install via PyPi
PyCaret is tested and supported on 64-bit systems with:
Python 3.8, 3.9, 3.10, and 3.11
Ubuntu 16.04 or later
Windows 7 or later
You can install PyCaret with Python's pip package manager:
PyCaret's default installation will not install all the optional dependencies automatically. Depending on the use case, you may be interested in one or more extras:
Check out all optional dependencies. If you want to install everything including all the optional dependencies:
Option 2: Source
Install the development version of the library directly from the source. The API may be unstable. It is not recommended for production use.
Option 3: Docker
Docker creates virtual environments with containers that keep a PyCaret installation separate from the rest of the system. PyCaret docker comes pre-installed with a Jupyter notebook. It can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are always tested for the latest major releases.
To learn more, check out the Docker page for pycaret/slim or pycaret/full.
Environment
In order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv (see python3 virtualenv documentation) or conda environments. Using an isolated environment makes it possible to install a specific version of pycaret and its dependencies independently of any previously installed Python packages.
Training on GPU
To train models on the GPU, simply pass use_gpu = True
in the setup
function. There is no change in the use of the API; however, in some cases, additional libraries have to be installed. The following models can be trained on GPUs:
Extreme Gradient Boosting
Catboost
Light Gradient Boosting Machine requires GPU specific installation
Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15
PyCaret Intel sklearnex support
You can apply Intel optimizations for machine learning algorithms and speed up your workflows. To train models with Intel optimizations use sklearnex
engine. There is no change in the use of the API, however, installation of Intel sklearnex is required:
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